Investigating the differential impact of psychosocial factors by patient characteristics and demographics on Veteran suicide risk through machine learning extraction of cross-modal interactions

Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care. The US Department of Veteran …

Early prediction of Alzheimer's disease using longitudinal electronic health records of US military Veterans

Abstract: BACKGROUND: Early prediction of Alzheimer's disease is important for timely intervention and treatment. We examine whether machine learning on longitudinal electronic …

Use of machine learning for early prediction of short-term mortality in Veterans with metabolic dysfunction-associated steatotic liver disease

Abstract: Background: Metabolic dysfunction associated steatotic liver disease (MASLD) is a leading cause of chronic liver disease worldwide and affects >25% in the United States …

Machine learning-based risk scores are associated with conversion to dementia in Veterans

Abstract: Background: We previously developed ancestry-specific risk scores for undiagnosed Alzheimer's disease and related dementias (ADRD) in Black and White American (BA and WA) …

Predicting depressive symptoms through social support: A machine learning approach in military populations

Abstract: Background: Perceived Social support has been consistently shown to reduce depressive symptoms among military personnel. However, limited research has explored how …

Sleep disturbances and PTSD: Identifying baseline predictors of insomnia response in an intensive treatment programme

Abstract: Objective: This study examined whether baseline demographic and clinical variables could predict clinically significant reductions in insomnia symptoms among veterans …

Machine learning applications related to suicide in military and Veterans: A scoping literature review

Abstract: OBJECTIVE: Suicide remains one of the main preventable causes of death among service members and veterans. Early detection and accurate prediction are essential …

Assessment of PTSD in military personnel via machine learning based on physiological habituation in a virtual immersive environment

Abstract: Posttraumatic stress disorder (PTSD) is a complex mental health condition triggered by exposure to traumatic events that leads to physical health problems and …

Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military Veterans treated with buprenorphine for opioid use disorder

Abstract: Aim: The aim of this study was to develop and validate machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans …

Improving explainability of post-separation suicide attempt prediction models for transitioning service members: Insights from the army study to assess risk and resilience in service members - longitudinal study

Abstract: Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been unable to resolve …

Development and validation of the BRief Eating Disorder Screener (BREDS) for US Veterans in healthcare and community settings

Abstract: Objective: To develop a Diagnostic and Statistical Manual of Mental Disorders (DSM-5) eating disorder screener. Method: Veterans enrolled in VA healthcare (N = 344) …

Utilizing machine learning to predict participant response to follow-up health surveys in the Millennium Cohort Study

Abstract: The Millennium Cohort Study is a longitudinal study which collects self-reported data from surveys to examine the long-term effects of military service. Participant …